Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Inference and Learning with Model Uncertainty in Probabilistic Logic Programs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BetaProbLog instead of SLProbLog for epistemic-uncertainty inference | Second-order Bayesian networks A2 and A3 · A2 and A3, N=10 | true error (TE): A2: 0.138 -> 0.134; A3: 0.150 -> 0.134 | Helped |
| Use BetaProbLog instead of SLProbLog for epistemic-uncertainty inference | Second-order Bayesian network A1 · A1, N=10 | true error (TE): 0.156 -> 0.152 | Hurt |
| Increase inference samples from 10 to 1000 (ablation) | BetaProbLog · A1, N=10 | true error (TE): 0.156 -> 0.144 | No clear effect |
| Provide more target probabilities for parameter recovery (ablation) | BetaProbLog parameter learning · A1, Np=4 | mean absolute error: Nq=3: 0.110; Nq=6: 0.008; Nq=9: 0.030 | Mixed |
| Learn more missing parameters (ablation) | BetaProbLog parameter learning · A1, Nq=9 | mean absolute error: Np=4: 0.030; Np=8: 0.036; Np=12: 0.102 | Hurt |
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